R Notebook

# library(BiocManager)
# BiocManager::install("DESeq2")
library(dplyr)

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library(readr)
library(DESeq2)

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library(readr)

gene_data <- read_csv('Breast_GSE45827.csv')

Rows: 151 Columns: 54677
── Column specification ─────────────────────────────────────────────
Delimiter: ","
chr     (1): type
dbl (54676): samples, 1007_s_at, 1053_at, 117_at, 121_at, 1255_g_...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(gene_data)

binary_class_data <- gene_data %>% 
  mutate(type = case_when(
    type == 'normal' ~ 'healthy',
    TRUE ~ 'cancer'
    )
  )

binary_class_data

binary_class_data <- as.data.frame(t(binary_class_data))
head(binary_class_data)

colnames(binary_class_data) <- binary_class_data[1,]
coldata <- as.data.frame(t(binary_class_data[c(2),]))
binary_class_data <- binary_class_data[-c(1,2),]

binary_class_data

coldata

converted_binary <- type.convert(binary_class_data)

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converted_binary

has.neg <- apply(converted_binary, 1, function(row) any(row < 0))
which(has.neg)

named integer(0)

dds <- DESeqDataSetFromMatrix(countData = converted_binary,
                              colData = coldata,
                              design = ~ type)

Error in DESeqDataSet(se, design = design, ignoreRank) : 
  some values in assay are not integers

library(affy)

Loading required package: BiocGenerics

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Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
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    'citation("pkgname")'.

gene_raw_data <- ReadAffy(celfile.path = "/Users/alex/Documents/GitHub/Breast-Cancer-Gene-Regulatory-Network-and-Risk-Prediction/GSE45827_RAW/")

assay_data <- assayData(gene_raw_data)$exprs
pheno_data <- phenoData(gene_raw_data)
feature_data <- featureData(gene_raw_data)

arraysRMA = rma(gene_raw_data, normalize = FALSE)

Background correcting
Calculating Expression

log_arraysRMAtable=exprs(arraysRMA)
count_table = as.data.frame(round(2^log_arraysRMAtable))
head(count_table)

colnames(count_table) <- substr(colnames(count_table),8,10)
head(count_table)

count_table <- count_table[-c(3,6,8,10,13,16,19,24,28,31,34,36,42,48,50,52,54,57,62,71,76,82,90)]
count_table

colnames(count_table) <- as.integer(colnames(count_table))
count_table

coldata <- as.data.frame(as.integer(colnames(count_table)))

colnames(coldata) <- 'Sample'

coldata <- coldata %>% 
  mutate(type = case_when(
    Sample > 168 & Sample < 180 ~ 'non-cancer',
    TRUE ~ 'cancer'
    )
  )

coldata

dds <- DESeqDataSetFromMatrix(countData = count_table,
                              colData = coldata,
                              design = ~ type)

converting counts to integer mode
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
  Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

dds

class: DESeqDataSet 
dim: 54675 155 
metadata(1): version
assays(1): counts
rownames(54675): 1007_s_at 1053_at ... AFFX-TrpnX-5_at AFFX-TrpnX-M_at
rowData names(0):
colnames(155): 84 85 ... 237 238
colData names(2): Sample type

keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]

dds$type <- relevel(dds$type, ref = "non-cancer")

dds <- DESeq(dds)

estimating size factors
  Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.
  Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
  Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
fitting model and testing
  Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 1490 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
  Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]
fitting model and testing
  Note: levels of factors in the design contain characters other than
  letters, numbers, '_' and '.'. It is recommended (but not required) to use
  only letters, numbers, and delimiters '_' or '.', as these are safe characters
  for column names in R. [This is a message, not a warning or an error]

res <- results(dds)
res

log2 fold change (MLE): type cancer vs non.cancer 
Wald test p-value: type cancer vs non.cancer 
DataFrame with 54675 rows and 6 columns
                  baseMean log2FoldChange     lfcSE      stat      pvalue        padj
                 <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
1007_s_at       1334.51058      0.8123694  0.181887  4.466330 7.95728e-06 3.94795e-05
1053_at          230.07337      1.9688682  0.239366  8.225336 1.94644e-16 4.16361e-15
117_at            83.06795      1.0857221  0.225627  4.812026 1.49408e-06 8.36205e-06
121_at           162.55550      0.0649612  0.112490  0.577483 5.63614e-01 6.48285e-01
1255_g_at          9.24762     -0.2263988  0.186028 -1.217014 2.23599e-01 3.12482e-01
...                    ...            ...       ...       ...         ...         ...
AFFX-ThrX-5_at    60.56504       0.808219  0.279738  2.889203   0.0038622   0.0109982
AFFX-ThrX-M_at    88.90409       0.311917  0.299819  1.040351   0.2981768   0.3923002
AFFX-TrpnX-3_at    7.66778      -0.218986  0.205893 -1.063588   0.2875156   0.3813199
AFFX-TrpnX-5_at   12.18044      -0.107204  0.168421 -0.636526   0.5244335   0.6130332
AFFX-TrpnX-M_at    9.10146      -0.287742  0.184936 -1.555899   0.1197322   0.1899917

resultsNames(dds)

[1] "Intercept"                 "type_cancer_vs_non.cancer"

# Log Fold Change Shrink
resLFC <- lfcShrink(dds, coef="type_cancer_vs_non.cancer", type="apeglm")

using 'apeglm' for LFC shrinkage. If used in published research, please cite:
    Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
    sequence count data: removing the noise and preserving large differences.
    Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895

resLFC

log2 fold change (MAP): type cancer vs non.cancer 
Wald test p-value: type cancer vs non.cancer 
DataFrame with 54675 rows and 5 columns
                  baseMean log2FoldChange     lfcSE      pvalue        padj
                 <numeric>      <numeric> <numeric>   <numeric>   <numeric>
1007_s_at       1334.51058      0.7713287  0.183864 7.95728e-06 3.94795e-05
1053_at          230.07337      1.9180309  0.243838 1.94644e-16 4.16361e-15
117_at            83.06795      1.0235148  0.229779 1.49408e-06 8.36205e-06
121_at           162.55550      0.0629059  0.110514 5.63614e-01 6.48285e-01
1255_g_at          9.24762     -0.2056307  0.179211 2.23599e-01 3.12482e-01
...                    ...            ...       ...         ...         ...
AFFX-ThrX-5_at    60.56504      0.7095392  0.282451   0.0038622   0.0109982
AFFX-ThrX-M_at    88.90409      0.2479490  0.274935   0.2981768   0.3923002
AFFX-TrpnX-3_at    7.66778     -0.1938796  0.196496   0.2875156   0.3813199
AFFX-TrpnX-5_at   12.18044     -0.0980537  0.162032   0.5244335   0.6130332
AFFX-TrpnX-M_at    9.10146     -0.2642140  0.179266   0.1197322   0.1899917

resOrdered <- res[order(res$pvalue),]
resOrdered

log2 fold change (MLE): type cancer vs non.cancer 
Wald test p-value: type cancer vs non.cancer 
DataFrame with 54675 rows and 6 columns
              baseMean log2FoldChange     lfcSE         stat       pvalue         padj
             <numeric>      <numeric> <numeric>    <numeric>    <numeric>    <numeric>
243689_s_at    25.9957       -3.89543  0.139157     -27.9930 1.97637e-172 1.08058e-167
224012_at      17.1237       -3.14268  0.129003     -24.3614 4.38935e-131 1.19994e-126
207092_at      85.7769       -5.70338  0.236180     -24.1484 7.75292e-129 1.41297e-124
211565_at      34.8702       -3.25637  0.136013     -23.9416 1.13155e-126 1.54669e-122
1552509_a_at   27.1727       -4.41723  0.185255     -23.8441 1.16619e-125 1.27523e-121
...                ...            ...       ...          ...          ...          ...
215244_at      22.8869    5.79663e-05 0.1330159  4.35785e-04     0.999652     0.999725
216200_at      14.0919   -4.95852e-05 0.1628031 -3.04572e-04     0.999757     0.999812
205735_s_at    18.0396    4.60511e-05 0.1960345  2.34913e-04     0.999813     0.999849
1553701_a_at   82.9793   -1.54787e-05 0.0804177 -1.92479e-04     0.999846     0.999865
1555629_at     22.8510   -1.39734e-05 0.1515850 -9.21817e-05     0.999926     0.999926